The goal of this project is to use innovative systems biology and synthetic biology approaches to[unreadable] quantitatively characterize and analyze bacterial gene regulatory networks underlying cellular[unreadable] responses to antibiotics, the formation of persisters and the emergence of resistance. With the[unreadable] alarming spread of antibiotic-resistant strains of bacteria, a better understanding of the specific[unreadable] sequences of events leading to cell death from bactericidal antibiotics is needed for future[unreadable] antibacterial drug development. Accordingly, there is a need for systems biology and synthetic[unreadable] biology approaches to discern the interplay between genes, proteins and pathways in furthering[unreadable] our understanding of how bacteria respond and defend themselves against antibiotics. The[unreadable] implications of the underlying logic of genetic networks are difficult to deduce through[unreadable] experimental techniques alone, and successful approaches will in many cases, involve the union[unreadable] of new experiments and computational modeling techniques. To address this problem, we have[unreadable] developed computational-experimental methods that enable construction of quantitative models[unreadable] of gene, protein and metabolite regulatory networks using expression measurements and no prior[unreadable] information on the network structure or function. In this project, we will use these approaches to[unreadable] reverse engineer bacterial gene regulatory networks underlying cellular responses to antibiotics,[unreadable] the formation of persisters and the emergence of resistance. The resulting networks and[unreadable] pathways will be analyzed to gain insight into the regulatory control of the associated biological[unreadable] processes, and the network models will be used to identify key regulators and mediators for a[unreadable] variety of phenotypic responses. This work could lead to new insights into the stress response of[unreadable] bacteria and the identification of novel targets for drug discovery, e.g., ones that overcome[unreadable] bacterial protective mechanisms or activate bacterial programmed cell death. This project may[unreadable] thus enable the development of novel classes of antibiotics that account for and utilize the[unreadable] complex regulatory properties of genetic networks.[unreadable]